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Archive for the ‘Trends in Neuroscience’ Category

Idiopathic normal pressure hydrocephaus (NPH) is a diagnosis of occult hydrocephalus with normal CSF pressure on LP that was first described in 1965 and is often considered one of the treatable causes of dementia.

The original paper used the now uncommon brain imaging technique of pneumoencephalography, which involved draining the CSF, injecting air as a contrast medium, and performing a brain xray:

Screen Shot 2017-09-17 at 10.48.34 AM

Figure 2 from Adams et al 1965 showing uniformly enlarged ventricles; doi: 10.1056/NEJM196507152730301

At my med school we learned NPH by the triad of “wet, wobbly, and wacky”, referring to its classic triad of symptoms: urinary incontinence, gait disturbance, and cognitive impairment.

Like many symptom triads, these symptoms are non-sensitive, with the full triad seen in <60% of patients. It is also non-specific, as urinary incontinence is seen in ~20-40% of those over 60, gait impairment is seen in ~20% over those over 75, and mild cognitive impairment is seen in ~35% of those over 70.

Espay et al explain all of this in the introduction of their critical literature review of idiopathic NPH. One of their major points is that ventricle enlargement is also non-specific, as it is common in other neurodegenerative diseases such as AD, DLB, and PSP.

Here are some of their other points:

  • There are no specific clinical, imaging, or neuropathologic findings in NPH.
  • The determination of ventricle enlargement on MRI is subjective and not standardized.
  • A “true” diagnosis is dependent upon a treatment response to CSF diversion via a ventriculoperitoneal shunt (VPS), which is circular and problematic.
  •  There has never been a well-defined RCT to evaluate the use of VPS in NPH.
  • Because many patients diagnosed with NPH may in fact have NPH that is secondary rather than a precursor to other neurodegenerative diseases, the fact that VPS may lead to short-term cognitive amelioration even in these patients suggests that VPS should still be considered as a way to improve cognition even in patients that are diagnosed with these neurodegenerative diseases.

Overall, this paper is well worth a read for people interested in treatments for dementia.

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CSF- and serum-borne autoantibodies against brain proteins are known to cause a wide range of cognitive sequelae due autoimmune attack. For example, when antibodies are raised against the protein LGI1, which is thought to act as a voltage-gated K+ channel, a common result is encephalopathy.

As a result, LGI1 is often included in autoimmune panels, along with several other proteins including CASPR2, NMDA and AMPA subunits, GABA-B receptors, GAD65, CRMp-5, ANNA-1, and ANNA-2.

Recently, Ariño et al presented a summary of 76 patients with LGI1-associated cognitive deterioration, 13% of which had forms of cognitive deterioration distinct from limbic encephalitis. At 2 years their major outcomes were:

  • 35% fully recovered
  • 35% regained independence but to baseline levels
  • 23% required assistance due to cognitive defects
  • 6% died

In mice, LGI1 is primary expressed at the RNA level in neurons at the RNA level, while in humans its expressed in both mature astrocytes and neurons (data from here and here), eg in the Darmanis et al 2015 human data set its actually expressed higher in astrocytes:

screen-shot-2016-10-10-at-6-46-25-pm

It might be interesting to see whether encephalopathies are generally only caused by autoantibodies against proteins expressed in neurons, or whether or cell type-expressed proteins can also lead to a similar clinical outcome.

 

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1) Scale mismatch between the synapse-synapse level and the kind of description you want to acquire about the nervous system for a particular goal. He argues that the point at which the interesting neural computation works might be at the mesoscale. It might be enough to know the statistics of how nerve cells work at the synapse level if you want to predict behavior.

2) Structure-function relationships are elusive in the nervous system. It’s harder to understand the information that is being propagated to the nervous system because its purpose is so much more nebulous than a typical organ, like a kidney.

3) Computation-substrate relationships are elusive in general. The structure of an information processing machine doesn’t tell you about the processing it performs. For example, you can analyze in finest detail the microprocessor structure in a computer, and it will constrain the possible ways it can act, but it won’t tell you what actual operating system it is using.

Here is a link to the video of Movshon’s opening remarks. He also mentions the good-if-true point that the set of connections of C. elegans is known, but our understanding of its physiology hasn’t “been materially enhanced” by having that connectome.

The rest of the debate was entertaining but not all that vitriolic. Movshon and Seung do not appear to disagree on all that much.

I personally lean towards Seung’s side. This is not so much due to the specifics (many of which can be successfully quibbled with), but rather due to the reference class of genomics, a set of technologies and methods which have proven to be very fruitful and well worth the investment.

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During development, one axon typically comes to dominate each set of synaptic sites at a neuromuscular junction. This means that just one neuron controls each muscle fiber, allowing for specificity of motor function.

A nice application of laser irradiation allows researchers to intervene in the formation of axonal branches in developing mice to study this.

What they found was that irradiating the axon currently occupying the site spurred a sequence of events (presumably involving molecular signaling) that led nearby axons (often smaller ones) to take it over.

A 67 second, soundless video of one 1,000-step simulation of this process demonstrates the concepts behind this finding.

In the simulation, each circle represents a synaptic site, and each color an innervating axon. There are originally six colors.

At each of the 1,000 time steps, one axon is withdrawn from a randomly chosen site, and an adjacent one (possibly of the same color) takes it over.

The territory controlled by one axon increases (with negative acceleration) until it comes to dominate all the sites.

Although it is possible that a qualitatively different process occurs for axonal inputs to nerve cells, odds are that a similar sort of evolution via competition helps drive CNS phenomena such as memory. (Because evolution tends to re-use useful processes.)

Reference

Turney SG, Lichtman JW (2012) Reversing the Outcome of Synapse Elimination at Developing Neuromuscular Junctions In Vivo: Evidence for Synaptic Competition and Its Mechanism. PLoS Biol 10(6): e1001352. doi:10.1371/journal.pbio.1001352

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In order to make serial section electron microscopy neurite reconstruction truly high-throughput, it will be essential to find a way to automate the image recognition component. Unfortunately, as I’ve written before, it’s quite difficult to segment and recognize patterns in electron microscopy images.

Inspired by other citizen science approaches, Sebastian Seung & co have come up with the possibly ingenious idea of enlisting the help of the everyman in this task. Their website is called Eyewire. It challenges users to reconstruct ganglion cells from electron microscopic images in the retina.

The images are stained in their cell membranes via a dye to create contrast. In theory, this contrast allows machines and humans to distinguish precisely where the neurite travels. In practice, the dye can invade to organelles, creating noise, or it can stain the cell membrane incompletely, creating artifacts.

Or, the machine learning algorithm might just miss it, because of some sort of bias, like missing boundaries that are outside of its field of view. This is where you come in. Your task is to move from slide to slide and pick out the regions that the algorithm misses.

I just opened up the game and in the first section I was assigned, I came upon this error. Here’s the first slide, which, as you can see, is completely filled in within its stained cell membrane boundaries:

And here’s the next image stack up:

As you can see, but for whatever reason the ML algorithm cannot, there is a hole in the second image which should be filled in. Eyewire allows you to do this yourself,

by filling the hole in with the light teal.

Sometimes the missing holes are more consequential. Filling in some holes means that whole undiscovered branches of a neurite can be found.

In a very nice feature, the algorithm automatically propagates your changes to the rest of the image stacks, so that you don’t have to do so manually.

When you have enough people doing this, the results can be pretty interesting. For example, here is the current reconstructed version of cell #6:

How would you go about quantifying the branching neurites of this neuron and what can you learn from its structure about how it works? These are the kinds of questions that we’ll be able to address as we collect more of these.

Sebastian Seung calls the game “meditative.” In the hours I’ve played so far (my account name is porejide), I have found it quite fun when it’s working fast and I can zoom through the stacks.

On the other hand, at times the internet connection at my house couldn’t really keep up, leading to some lag, which caused me to experience a sensation that I would not call meditative. But perhaps that’s just the fault of my internet connection.

One angle that I especially appreciate is the friendly competition between users. After you fill in a set of image stacks, the game rewards you with a number of points that is meant to be proportional to what you accomplished.

I have no small amount of pride in reporting that yesterday I played well enough (and for long enough) to reach #2 in points for the day, with 981 points, although xo3k was way ahead of me with 3450. As I was playing I could see user vienna717 was gaining ground on me quickly, which gave me the competitive juices I needed to go faster.

This is a great infrastructure, and has the potential to get even more fun if they gamify it further. For example, perhaps users could join teams with other people and play for a glory greater than the self.

This all sounds dandy, but what if you don’t care about retinal ganglion cells? Frankly, I don’t care that much myself. To the best of my understanding, the main thrust of the game is not to build the 3d maps of these ganglion cells, although that will be informative.

Rather, the idea is to provide a huge training set for machine learning algorithms, so that they can learn to better incorporate the insights of humans. This will scale much better than having humans do it, and will in theory allow us to reconstruct neural connections on much larger scales.

This, in turn, will allow us to rigorously test some of the most fundamental questions in neuroscience.

There is no guarantee that Seung & co’s approach will actually get us there, and even if it does, it will take a lot of time and effort. In the meantime, I’ll see you on the leaderboard!

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What has been the growth rate of computing power, multi-neuron recording, and DNA sequencing over the past decade? Konrad Kording provides an illuminating chart pertaining to this question:

neurons recorded = the number of neurons that can be recorded from simultaneously; the neuron and computer scales are exponential fits to data; doi:10.1371/journal.pcbi.1002291

Given the above DNA sequencing trends, it’s no surprise that groups in many different fields are developing strategies to turn the problem they are trying to study into a sequencing problem.

See, for example, Jonathan Weissman’s talk on ribosome profiling, which is an elegant way to use DNA sequencing of mRNA molecules tethered to the ribosome as a way to study translation.

In his article, Kording touches on a couple of intriguing sequencing technologies that might help make the “data-out” step of a given neuroscience experiment more high-throughput.

The method for connectomics he describes is particularly fascinating. The idea is to assign neurons a unique DNA barcode that is spread to each of its synaptic partners via a transsynaptic virus, and then sequence the set of barcodes from a given group of cells.

One aspect that I think Kording might have underemphasized is that these technologies would improve greatly if we improved our ability to sequence the DNA of individual neurons.

For example, typical protocols for probing the expression of intermediate early genes rely on harvesting cells from mass culture or coarse brain regions before sequencing. This is powerful, but it would be much more so if we could analyze the distribution of gene expression between cells rather than across them.

Single-cell genomics is advancing, but it is not yet at the point of routine laboratory use for a typical sequencing experiment. And in order to really take advantage of DNA sequencing technology in understanding how networks of neurons work together, it will presumably need to reach that point.

References

Kording KP (2011) Of Toasters and Molecular Ticker Tapes. PLoS Comput Biol 7(12): e1002291. doi:10.1371/journal.pcbi.1002291

Link to Jonathan Weissman’s 11/16/11 talk.

Oyibo H, et al. 2011 Probing the connectivity of neural circuits at single-neuron resolution using high-throughput DNA sequencing. Presentation at Computational and Systems Neuroscience Meeeting, pdf.

Saha RN, et al. 2011 Rapid activity-induced transcription of arc and other IEGs relies on poised RNA polymerase II. doi: 10.1038/nn.2839.

Kalisky T, et al. 2011 Single-cell genomics. doi:10.1038/nmeth0411-311

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Micah Manary has written a good summary of the recent Heijtz et al paper linking these two. The authors compared germ-free mice, which are raised in a completely sterile environment, and specific pathogen free mice, which are free of mice pathogens but otherwise have a normal gut microbiota, on a number of measures. First, the germ-free mice run more in an open field test, which is considered a sign of increased anxiety:

SPF = specific pathogen free = has bacteria in gut but no known mice pathogens; GF = germ free micel; top = distance traveled in the box per zone and in total over the 60 mins; bottom = representative tracings of movements in each group

Note the trend in the representative tracings, as the specific pathogen free mice tend to move less as time progresses, which is a sign that they are becoming “used to” the novel environment more quickly than the all germ free mice. Next, the authors conduct a number of gene and protein expression assays to show that there are statistically significant differences in various regions of the brain. For example, the germ-free mice have significantly reduced expression of the “early responder” gene nerve growth factor-inducible clone A:

open bars = normal gut microbiota but pathogen free mice; closed bars = germ free mice; OFC = orbital frontal cortex; AO = anterior olfactory region; left = representative autoradiograms

The authors used four mice in each condition, and some of the brain regions seemed to have significant expression trends for certain factors but not others. So we should take these results with the standard amount of caution. But the interaction between bacteria and the brain, especially during development, and especially related to anxiety, is certainly a trend to watch for in the coming years.

Reference

Heijtz RD, Wang S, Anuar F, Qian Y, Björkholm B, Samuelsson A, Hibberd ML, Forssberg H, & Pettersson S (2011). Normal gut microbiota modulates brain development and behavior. Proceedings of the National Academy of Sciences of the United States of America, 108 (7), 3047-52 PMID: 21282636

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